The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies' competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.

Industry 4.0 is expanding to the entire manufacturing fabric. Such evolution entails the complete digitalization of industrial processes and products, through the deployment of cyber-physical systems and automation in the shop floors, logistics and business processes. Such digitalization is achieved by extracting value, in the form of insights, decision-supporting information and detailed virtual representations of the physical industrial processes. One prominent example of such digitalization is the advent of Digital Twins, accurate virtual representations of industrial processes and products in the physical world. This work presents the development and deployment phases and procedures of a Big Data-supported Digital Twin for logistics processes in the automotive sector. The Digital Twin enables planning and optimization of logistics processes as, for instance, the optimization of stock and inventory, and planning the arrival of new parts, in order for the production to be as efficient as possible, without the risk of stopping the shop floor, ultimately enabling savings in both idle stored parts and in supplier orders' reductions.

El proyecto i4Q (Industrial Data Services for Quality Control in Smart Manufacturing) es una acción de innovación del programa H2020 de la Comisión Europea cuyo objetivo principal es proporcionar servicios de datos industriales confiables basados en el internet de las cosas. El resultado global del proyecto consta de 22 soluciones, capaces de gestionar enormes cantidades de datos industriales que provienen de dispositivos de tamaño pequeño interconectados para dar soporte a la supervisión y el control en línea de la fabricación. El marco i4Q garantizará la confiabilidad de los datos con funciones agrupadas en cinco capacidades básicas alrededor del ciclo de datos: detección, comunicación, infraestructura informática, almacenamiento, y análisis y optimización. Asimismo, el resultado global i4Q incluirá herramientas de simulación y optimización para la calificación de procesos en líneas de fabricación continuas, diagnóstico de calidad, reconfiguración y certificación para garantizar un alto nivel de eficiencia en la fabricación, lo que lleva a un enfoque integrado para la fabricación sin defectos.

This paper presents a new innovative framework to support smart manufacturing quality assurance. More specifically, the i4Q framework provides an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 innovative Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework guarantees data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis-optimization. i4Q RIDS includes simulation and optimization tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency, leading to an integrated approach to zero-defect manufacturing. This paper presents the main principles of the i4Q framework and the relevant industrial case studies on which it will be evaluated.

Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC’s master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0.

This article identifies the advances, advantages, limitations, requirements and current methodologies in implementing the strategic Industry 4.0 (I4.0) initiative. It focuses on all research works mainly on production planning. To do so, it proposes a taxonomy of the principles of I4.0 design terms that contemplates the following classification aspects: interconnection/connectivity, decentralised decision making, technical assistance, the human factor, intelligence/awareness, interoperability, information transparency, technology, organisation, conceptual frameworks and production planning. It also presents the models, algorithms, heuristics and meta-heuristics of the components used in relation to an I4.0 setting. Finally, a considerable number of reference conceptual frameworks is analysed, which allow the term I4.0 to be defined.

Industry 4.0 (I4.0) implies a group of technologies, organisational concepts and management principles to improve the performance of manufacturing companies or supply chains driven by production cost optimisation, mass customisation requirements, connectivity and digitisation of factories. The purpose of this paper is to relate Iberian Peninsula advances in I4.0 from Spanish and Portuguese research works published in CJOR papers. Hence this paper reviews the Spanish and Portuguese operations research (OR) and industrial engineering-based papers published in CJOR from 2011, when the I4.0 concept emerged, to the present-day. Here 47 papers are reviewed according to classification criteria based on the following elements: (1) objectives; (2) application context; (3) modelling approach; (4) development or software tool; (5) I4.0 technologies. The main outcomes, limitations and further research are also identified for recent papers. Finally, research trends and future directions in industrial engineering, OR and I4.0 are discussed.

Facility layout planning (FLP) involves a set of design problems related to the arrangement of the elements that shape industrial production systems in a physical space. The fact that they are considered one of the most important design decisions as part of business operation strategies, and their proven repercussion on production systems’ operation costs, efficiency and productivity, mean that this theme has been widely addressed in science. In this context, the present article offers a scientific literature review about FLP from the operations management perspective. The 232 reviewed articles were classified as a large taxonomy based on type of problem, approach and planning stage and characteristics of production facilities by configuring the material handling system and methods to generate and assess layout alternatives. We stress that the generation of layout alternatives was done mainly using mathematical optimisation models, specifically discrete quadratic programming models for similar sized departments, or continuous linear and non-linear mixed integer programming models for different sized departments. Other approaches followed to generate layout alternatives were expert’s knowledge and specialised software packages. Generally speaking, the most frequent solution algorithms were metaheuristics.